1. Field of the Invention
The invention concerns a method for automatic or semi-automatic segmentation of a 3D image data set, acquired by a medical imaging apparatus, and a device to implement such a method.
2. Description of the Prior Art
Segmentations of image data sets from medical imaging apparatuses are not reliable for additional image processing nor for a better detection capability and diagnosis. A segmentation (for example of the left atrium of a human heart) from three-dimensional CT, MR or rotation angiography image data is important for planning and implementation of medical procedures and ablation procedures, since the left atrium of the heart has a high individual variability. Currently, model-based or greyscale value-based segmentation algorithms are used; both types have advantages and disadvantages. The model-based segmentation can be implemented quickly, automatically at the press of a button, and is able to function even given image data with reduced image quality and/or reduced contrast, but is also often very imprecise. In contrast to this, a greyscale value-based segmentation algorithm is very precise given good image quality, but is slow and error-prone given reduced image quality and/or reduced contrast of the image data set, and it often requires a large number of user inputs.
Both model-based and greyscale value-based algorithms are commercially available. For example, in x-ray apparatuses for image processing, Philips Healthcare uses a model-based segmentation algorithm for segmentation of the left atrium (“EP Navigator” product). In angiography x-ray apparatuses, Siemens Healthcare uses a semi-automatic greyscale value-based segmentation, known as “1-click segmentation,” for image processing (“InSpace EP” product). In this product, in the ideal case the user only needs to mark an image point in the center of the left atrium, and the segmentation is subsequently implemented automatically.